Glenn Moncrieff's repositories
lit_cloud_seg
Example repo for workflow training a deep learning cloud segmentation model using pytorch lightning and xarray dataprep
demeter
A land use land cover disaggregation and change detection model
emit_st_app
st app for spectra
python-imspec-course
A course on using Python to analyse imaging spectroscopy data
saeonstac
Creating and managing STAC at SAEON
earth-observations-api
TiTiler service for SAEON hosted geospatial
torchuq
A library for uncertainty quantification based on PyTorch
aliens-unmix-emit
Unmixing alien vegetation in fynbos using EMIT surface reflectance
saeonobspy
An python package to query available datasets and download selected datasets from the SAEON observations database
ngimaps
Online map of South Africa based on CD:NGI map sheets
mnemosyne
A very simple HTTP-range supporting file server. Stream your file in, and stream your file out!
xarray-enmap
notebooks to get started working with enmap hyperspectral data in python
earthengine-workflow
Deploy Tensorflow models to Google Cloud Platform for automating predictions on Earth Engine imagery
postfire-statespace
A state-space model of vegetation activity and fire recovery in the Cape Floristic Region
GMoncrieff.github.io
Glenn Moncrieff
satellite-image-deep-learning
Deep learning with satellite & aerial imagery
renosterveld-monitor
supervised continuous change detection in a shrubland ecosystem
IEEE_TGRS_SpectralFormer
Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Spectralformer: Rethinking hyperspectral image classification with transformers, IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
global_renosterveld_watch
automation pipeline for the renosterveld monitor
earthengine-api
Python and JavaScript bindings for calling the Earth Engine API.
cedar-project
Monitoring and mapping the critically endangered Clanwilliam Cedar using deep learning and remote sensing
streamflow-reduction
Code to estimate streamflow losses to invasive alien plants in South Africa
catchments_demo
Code and data to demonstrate best practice for reproducible workflows in R. Uses data from the South African paired catchment experiments as an example
eo-flow
Collection of TensorFlow 2.0 code for Earth Observation applications